Our work leverage reinforcement learning to bridge human guidance and the model adaptation process. Some existing studies combined traditional machine learning algorithms with reinforcement learning to obtain more interpretable and optimal models.
Take the tree-structured model for an example, the splitting features and values were predicted for each non-leaf node orderly by employing reinforcement learning-based methods [1]}, [2]}, [3]}. The common purpose of these works is to learn the model-building procedures and model parameters. The tree’s structure won’t be changed after training, while our model can be adapted during the test process. Sendi et al. [4]} presented a deep ensemble method based on an argumentation of combining machine learning algorithms with a multi-agent system to improve classification performance. However, the framework can not be directly employed because the interaction between the human and the identification model should be considered in our framework.
